Method of sorting objects comprising organic material

ABSTRACT

A method of sorting sorting objects within a bulk of objects from a heterogeneous population is provided. The bulk of objects to be sorted has an inherent variation, and at least one class, having less variation than the originally inherent variation of the bulk, is separated from the bulk. This lesser variation represents a quality of composition with reference to any organic material of the objects within the bulk. The method comprises the steps of distributing each of the objects to be separated as a separate object in a sorting device; exposing the separate object to energy emittedfrom at least one energy source; recording from at least one point of the separateobject by meansof at least one sensor a first multivariate signal; predicting orclassifying, by means of a calibration method previously performed on a subset of the population, between the first multivariate signal and the quality of composition, a second signal expressing the magnitude of at least one quality variable of univariate variation; and separating the separate object from the sorting device to the at least one collected class in dependence on the magnitude of the at least one quality variable of the second signal from the at least one point.

[0001] The invention refers to a method of sorting objects. Morespecifically, the invention refers to a method of sorting objects withina bulk of objects from a heterogeneous population by separating from asorting device at least one collected class of different quality ofcomposition with reference to any organic material of the objects.

[0002] There exist a number of methods for sorting objects according toouter attributes, such as length, size and density.

[0003] For example, in the cleaning of grains it is common to usemachines designed to screen out impurities, such as over- and undersizedmaterial, or to classify grains, for example malting barley, accordingto the width of the kernels. Further gravity tables are used to sortgranular materials according to the density of the granules.

[0004] There are also cleaning machines for granular materials, whichremove impurities according to their colour. In these machines thematerial to be cleaned is made to fall into the free atmosphere, ideallyone by one. During their fall the objects are illuminated with light.The reflected, transmitted, or emitted light from each object isdetected at 1-3 pre-selected bands of wavelength in the visible and/orinfrared (IR) regions by use of optical filters. These bands arepre-selected in order to give a signal corresponding to a known sortingcharacteristic of the objects to be removed, for example discolouration.Furthermore, in these methods the optical filters are selected so thatthere is a substantial difference in the transmitted, reflected, oremitted light between a wanted or unwanted object.

[0005] An optical sorting apparatus for agricultural products is shownin U.S. Pat. No. 4,963,041, which has a background device for comparisonof the colour and/or the brightness of the object to be sorted. Thebackground device is dynamically variable in order to provide anadjustable reference for an optical detector. Objects to be cleaned aresimilarly positioned in front of a suitable background in GB 2 091 415as well as in U.S. Pat. No. 4,203,522.

[0006] In U.S. Pat. No. 4,421,772 a method for identifying botanicalcomponents parts of ground seeds is shown, wherein a lighting system forfluorescent microscope is used. Fluorescence is also utilized in U.S.Pat. No. 4,866,283 for the inspection and cleaning of beans, nuts, andpulses, for example peanuts, where laser-induced luminescence is used todetect impurities. The inspection system comprises an excitation meansfor illuminating an object to cause it to produce fluorescent radiation.A specific characteristic reading produced by the object is compared toa reference reading in order to obtain an indication of one specificcharacteristic of the object. In this way, the system identifies andremoves undesired or damaged objects, e.g. peanuts contaminated withaflatoxin, from a stream of objects by determining fluorescence at apredetermined wavelength. The detection of aflatoxin by means offluorescence after exposure to long-wave ultraviolet radiation has alsobeen accomplished in U.S. Pat. No. 4,535,248.

[0007] In GB 2 060 166 differences between two materials aredistinguished by utilizing a device with two filters. In this device amixture of two different materials are fed through a testing zone intowhich light pulses with two alternating frequencies are directed. Thesetwo frequencies correspond to the characteristic frequencies of theamplitude peaks for the light reflected by each material, the reflectedlight pulses being evaluated to provide a rejection signal for thephysical separation of the two materials when a reflected pulse has aspecific relationship.

[0008] There is no indication in the prior art methods that amultivariate calibration, which describes the heterogenity ofcomposition of the objects to be sorted, should be performed before theactual sorting.

[0009] In such traditional methods the reflected, transmitted, oremitted light is registered. Three wave-lengths at the most areanalyzed, one within the visible light region and two within the IRregion. Thus, a feature of the object is determined with reference tothe recording from one to three wave-lengths only.

[0010] Furthermore, simultaneous measurements of several (>3)wavelengths can not be performed with such a filter arrangement whileassuming that the light hitting each filter will be reflected from oneand the same well-defined point on an object and/or with one and thesame angle.

[0011] Neither is the removal of an object in a colour sorter specific,since the air jet ejector used also removes from their falling lineseveral objects in the vicinity of that object which is to be removed.Thus, the purity of the removed fraction is low and it contains—at thebest—2-3 times as many non-selected objects as the number of objectsselected for removal.

[0012] Furthermore, the objects in the vicinity of those removed areinfluenced and brought out of their falling line and their positions canthus not be identified. This means that the sorting of any of theseobjects is even more inaccurate and limits the application of coloursorters to cleaning tasks, where the percentage of impurities is low.

[0013] An inherent property in a population of objects of biologicalorigin as well as many man-made objects comprising organic material isthat they exhibit a variation in one or several different qualities ofcomposition. Such an inherent variation or heterogenity is, in contrastto impurities, an integrated property of a population. Thus, there existseveral types of qualities or properties, which can not be determined byone, two or three wave-lengths.

[0014] The purpose of the invention is to achieve a method of sortingobjects whereby the above-mentioned problems are eliminated.

[0015] A further purpose of the invention is to provide a method, inwhich the total population of objects to be sorted are classified withreference to its heterogenity in one or more of its qualities ofcomposition.

[0016] Another purpose of the invention is to provide a method whichwill allow the identification of intact objects of a heterogeneouspopulation and the sorting of them into two or more classes, each beingmore homogeneous than the original non-sorted material.

[0017] Still another purpose of the invention is to provide a method,whereby organic materials can be measured, evaluated as well as sortedin one process into more useful and valuable classes.

[0018] Yet another purpose of the invention is to provide a method ofhigh sorting capacity, which is suitable for sorting of bulk materials,such as raw materials or semi-manufactures for industrial productionwithout affecting the normal production procedures of an industrialprocess.

[0019] In order to achieve these purposes, the present inventionprovides a method of sorting objects within a bulk of objects from aheterogeneous population. This bulk of objects to be sorted has aninherent variation. At least one class, having less variation than theoriginally inherent variation, is separated from the bulk, and thislesser variation represents a quality of composition with reference toany organic material of the objects within the bulk.

[0020] In this connection the term “organic material” pertains tosubstances derived from living organisms and chemical substancescontaining covalently bound carbon atoms as well as textures, structuresetc, which are formed thereof. Of course, the inventive method can alsobe used for classifying and sorting inorganic objects.

[0021] The inventive method comprises the steps of

[0022] (a) distributing each of said objects to be separated as aseparate object in a sorting device;

[0023] (b) exposing said separate object to energy from at least oneenergy source;

[0024] (c) recording from at least one point of-said separate object bymeans of at least one sensor a first multivariate signal;

[0025] (d) predicting or classifying, by means of a multivariatecalibration method previously performed on a subset of said population,between said first multivariate signal and said quality of composition,a second signal expressing the magnitude of at least one qualityvariable of univariate variation; and

[0026] (e) separating said separate object from said sorting device tosaid at least one collected class in dependence on the magnitude of atleast one quality variable of said second signal from said at least onepoint.

[0027] In the drawings

[0028]FIG. 1 shows examples of unsupervised pre-treatments performed onthe first multivariate signal;

[0029]FIG. 2 shows an example of a spectrum obtained from an individualwheat kernel in a sample;

[0030]FIG. 3 shows the distribution curve of a second signal obtainedfor the kernels in the sample;

[0031]FIG. 4 shows an example of classification of a sample into threeclasses (A, B and C) according to a combination of two unsupervised(unknown) quality variables;

[0032]FIG. 5 shows an example of a near infrared transmittance spectrumof a popcorn berry;

[0033]FIG. 6 shows the results of a classification of popcorn berries, aPrincipal Component Analysis of the three classes (A, C, and E) obtainedafter sorting according to the inventive method.

[0034] The present invention provides a new concept of sorting byexploiting the inherent heterogenous nature of organic materials of forexample biological origin. The invention is based on the observationthat objects comprising organic material often exhibit a large variationin their absorption of electromagnetic radiation in general at a largenumber of specific energies. For example, a large number of singlegranules in granular materials have been analyzed by using newanalytical techniques. The inventors have surprisingly found that a hugevariation from granule to granule in such materials can be utilized forsorting into more homogenous classes. This inherent variation is, ofcourse, not obtained as a colour difference in the material, whichindicates impurities, or as a difference, which can be attributed to avariation in intensity of reflected, transmitted, or emitted light at1-3 fixed wave-lengths.

[0035] Such a multivariate variation reflects an inherent variation inknown and/or unknown parameters of quality, which characterises eachobject. These parameters of quality can not be related directly to thereadings registered according to the state of the art.

[0036] A simultaneous measurement of for example transmitted, reflected,and/or emitted electromagnetic radiation in general is accomplished at alarge number of energies in one or more selected regions of radiation sothat a spectrum can be recorded for each object. The variation inintensity at different wavelengths between the different spectra is usedto sort the objects. The different levels of the reflected, transmitted,or emitted radiation then correspond to different variables. The largenumber of intensity values at different levels of energy in eachspectrum is further processed for each object into one signal only,which is used for sorting the objects into different classes. Thus,typically a large number of different values, at least four singularintensity values, are reduced into a signal for sorting the objects. Thetechnique is used to sort objects so that the inherent variation betweenobjects in a class is reduced and/or minimized. This lesser inherentvariation can for example be achieved by reducing the large number ofabsorption values recorded at the different levels of energy into a fewquality variables, which parameters describe the inherent variation inthe material to be sorted. These quality variables are chosen in such away that they describe as high a portion as possible or a specificportion of the variation in the spectra of a set of reference objects.

[0037] More specifically, the inventive method is designed to sort bymeans of a sorting device objects within a heterogeneous population intoat least two collected classes of different qualities of compositionwith reference to any organic material of the objects. At least onecollected class exhibits less variation than the original non-sortedpopulation.

[0038] In this connection a quality of composition is a peculiar,distinct, or essential character, which can be defined or which remainsundefined. Thus, the heterogeneity can be unknown with reference to itsnature, i.e. not yet ascribed to a single or a combination of two ormore quality parameters. In contrast to the state of the art, in whichone occasional undesired object of a population is removed, the presentinvention utilizes integrated heterogenities in qualities of compositionof a population for sorting.

[0039] A quality of composition, as used in the present invention, canbe a variation in chemical composition from object to object, i.e. avariation in quality as well as quantity. However, it can as well be aderived property like wetability, flavour, thermal plasticity,millability, or a potential of a certain class of the objects to causegood baking quality of a seed after processing, a large volume ofpopcorn after popping, a particular strength of a plastic object,pharmaceutical pills having no tendency to burst, a less bitter taste ofchocolate after processing of cocoa beans, etc. Thus, the quality ofcomposition can be a chemical quality, a structural quality, a sensoricquality, or a functional quality. Of course, these qualities ofcomposition can be combined, since for example a good baking quality canonly be partly derived from the protein content.

[0040] Every mechanical system can be used as a sorting device inconnection with the inventive method, which is designed for arrangingobjects in such a way that they, in contrast to the above-mentionedcolour sorter, can be systematically organized according to specifiedinstructions and removed from their positions with high precisionwithout influencing adjacent objects.

[0041] Each of the objects to be separated is according to the inventionfirst distributed as a separate object in a sorting device. Then each ofthe separate objects is exposed to energy emitted from at least oneenergy source. The energy emitted can be electromagnetic radiationand/or sonic waves.

[0042] Any electromagnetic radiation or sonic waves, alone or incombination, can be used, such as ultraviolet light, visual light, nearinfrared light, infrared light, fluorescent light, ultrasonic waves,microwaves, or nuclear magnetic resonance.

[0043] Preferably, the energy source(s) emit(s) energy which byreflection, transmission, or emission from the objects results in aresponse with a high selectivity in respect of heterogeneity of thematerial to be sorted.

[0044] In order to ensure a high sorting capacity, it is an importantaspect of the inventive method that the time used for recording andanalysing data can be adjusted to an optimal speed of distributionand/or rejection of particular types of objects. Thus, the recording ofthe first multivariate signal from any point of an object to be sortedshould not be performed for a longer time period than 20-30 ms,preferably 5 ms.

[0045] A first multivariate signal is recorded from at least one pointof each separate object by means of at least one sensor. The sensor canbe either univariate or multivariate, i.e. constructed for measurementof one or simultaneous measurement of more than one wavelength,respectively, as singular intensities for each of four or morewavelengths or as a sum of four or more wavelengths. The recording cantake place both when the separate object is stationary and when it ismoving under the sensor(s). If necessary, several sensors are used sothat a satisfactory correlation can be obtained to the quality of theobject.

[0046] For example, if only one sensor is used, it has the capacity toregister reflected and/or transmitted and/or emitted electromagneticradiation and/or sonic waves at more than three wavelengths as singularintensities or as a sum of intensities. In the inventive method thefirst multivariate signal for each object is then processed into asignal for precision sorting.

[0047] Each recorded first multivariate signal reflects a variation inknown or unknown parameters of quality characterizing each object. Sincemost qualities are complex, a multivariate approach is used to convertdetected signals into sorting signals. This is accomplished by eachfirst multivariate signal—measured from at least one point of theobjects—being translated to (i.e. reduced to) a second univariatesignal.

[0048] This second univariate signal is predicted or classified by meansof a multivariate calibration method between the first multivariatesignal and the quality of composition. The calibration, which is basedon at least four variables, has previously been performed on a subset ofthe population and describes the heterogenity of composition of the bulkto be sorted. The second signal then expresses the magnitude of at leastone quality variable of univariate variation. When the second signalexpresses the magnitude of more than one variable, these variables canbe solely predicted or solely classified or a combination of bothclassified and predicted variables.

[0049] Before this predicting or classifying step the first multivariatesignal is preferably transformed by means of a supervised or anunsupervised pretreatment.

[0050] When the quality of composition is a defined quality ofcomposition, the multivariate calibration is carried out by use of asupervised multivariate method. In this mathematical processing of thefirst multivariate signal, the quality of composition of a number ofobjects should be known. The first multivariate signal is decomposed inorder to explain the quality of composition of a population and aregression model is established, for example by means of Partial LeastSquares regression. The quality of composition can be used to supervisethe algorithm in finding the relevant information in the firstmultivariate signal. This model is then applied on new acquired firstmultivariate signals of new objects to be sorted, and the quality ofcomposition in question is predicted. The predicted magnitude of thequality composition is then used for sorting.

[0051] Examples of supervised methods to be used in the method accordingto the invention are Partial Least Squares (PLS) Regression, MultipelLinear Regression (MLR), Principal Component Regression (PCR), NeuralNetwork, and N-way PLS.

[0052] Thus, when the quality of composition is defined, the firstmultivariate signals are used directly for the prediction of a givenquality parameter. The first multivariate signls (e.g. spectra)—obtainedfrom a subset of the population—are decomposed in order to explain thequality parameter and a regression model is established, for example byPLS regression. This model is then applied on new acquired spectra asthe first multivariate signals of the objects to be sorted, and thequality parameter in question is predicted. The predicted magnitude ofthe quality parameter is then used for sorting (c.f. FIG. 3). In thisway a sorting is performed which is based on a known parameter, e.g.protein content as in FIG. 3.

[0053] When the quality of composition is of the unspecified type, i.e.undefined, the multivariate calibration is carried out by use of anunsupervised multivariate method. In this way the mathematicalprocessing reduces the first multivariate signal to a few underlyingstructures. Thus, the variation in the spectra is utilized withoutdefining the quality of composition (no supervision) and without directlinking to an analyzed or known quality of composition.

[0054] Principal Component Analysis (PCA), SIMCA, PARAFAC and TUCKER areexamples of unsupervised multivariate methods which can be used in themethod according to the invention.

[0055] Thus, when the quality of composition is undefined, the firstmultivariate signal (e.g. spectra) are analyzed by for example PCA.Latent variables are obtained, which are used as new univariatevariables for sorting.

[0056] Of course, a latent variable—or a combination of several latentvariables—is only used if they explain the variation within theheterogenous bulk quantity and improves the quality in a givenapplication.

[0057] When the most probable sum of variations is explained, and whenthe quality is improved, this calibration model (latent variable orcombination of latent variables) is applied on the first multivariatesignal from the objects to be classified for sorting. In this way asorting can be performed which is based on an unknown parameter, e.g. athird and a fourth latent variable.

[0058] However, the first multivariate signal may contain someinformation that is not related to the quality of composition. Thus, itis preferred to pre-treat this first multivariate signal before thecalibration methods are applied, in order to construct more simple androbust calibration models. Of course, the pre-treatment must also beperformed before prediction and classification.

[0059] When the quality of composition is undefined, unsupervisedpre-treatments are performed on the first multivariate signal. Examplesof unsupervised pre-treatments are derivations, 1^(st) and 2^(nd) (orhigher order) derivative, Standard Normal Variate (SNV), andMultiplicative Scatter Correction (MSC). FIG. 1 shows examples of NITspectra in the range 850-1050 nm, represented as Raw, MSC corrected,1.der and 2.der, respectively.

[0060] When the quality of composition is defined, supervised orunsupervised pre-treatments are performed on the first multivariatesignal. The quality of composition can then be used to guide thepre-treatment in order to eliminate the irrelevant information from thefirst multivariate signal. Examples of supervised pre-treatments areDirect Orthogonalisation (DO) and Orthogonal Signal Correction (OSG).

EXAMPLES

[0061] The invention will now be further described and illustrated byreference to the following examples. It should be noted, however, thatthese examples should not be construed as limiting the invention in anyway.

Example 1

[0062] Sorting of Wheat Kernels with Reference to Their Protein Content.

[0063] The protein content in single wheat kernels has been found tovary substantially within a bulk sample. The baking quality for a sampleof Northern European wheat has typically a variation from 8-16% betweenindividual kernels.

[0064] A batch of baking wheat was withdrawn from a commercial silo andsorted into three classes according the invention. The batch was fed tothe distributor of the sorting device in such a way that each individualkernel obtained a fixed position and that individual kernels were fixedin such a way that they were discretely separated from each other. Eachkernel in the distributor was then exposed to the light from a tungstenlamp. The light was filtered through a silicon filter with a cut-offlimit at 1100 nm prior to exposure and the reflected light between 1100and 1700 nm was recorded by use of a diode-array spectrometer. A typicalexample of a spectrum of a single wheat kernel spectrum from the sampleis shown in FIG. 2.

[0065] A bandwidth of 10 nm is sufficient and the counts within eachband were recorded. For each kernel the signals obtained from 60 bandsbetween 1100 and 1700 nm were used as the first multivariate signal. Thefirst multivariate signal was then pre-treated by means of theunsupervised method MSC, spectral scatter thereby being eliminated.

[0066] Prior to sorting, the protein content of a sample was determinedby means of Kjeldahl analysis (AACC Method 46-12, adjusted to singleseeds), and a calibration model was established between the pre-treatedfirst multivariate signal and the protein content by using thesupervised multivariate method PLSR. This model was then used to predicta second signal representing the protein content for each kernel in thesample. The distribution curve is shown in FIG. 3, the number of kernelsin the batch being plotted against the second signal, which thusrepresents the protein content.

[0067] The batch was then sorted into three classes (A, B and C,respectively, as indicated in FIG. 3) based on the magnitude of thesecond signal, and the classes were collected in three separatecontainers. The contents were weighed and the following distribution wasobtained: A:26%, B:38%, and C:36%. A sample was taken from the originalnon-fractionated batch and from each of the three classes obtained aftersorting. These four samples were analyzed for protein by means of theKjeldahl method, and the results are shown in Table 1 below. TABLE 1Protein Content Non-fractionated sample 12.3% Class A 10.2% Class B12.0% Class C 14.4%

[0068] Thus, incoming wheat can be sorted according to the inventioninto two or more classes with different protein contents. These classesare suitable for different purposes, such as production of biscuitflour, cake flour, bread flour, and/or semolina for pasta.

Example 2

[0069] Sorting of Wheat Kernels According to Their Baking Quality.

[0070] It is known that different samples of wheat exhibit differentbaking qualities, for example in terms of loaf volume.

[0071] A sub-sample of a normal baking wheat was withdrawn from acommercial silo and fed to the distributor of the sorting device. Thefirst multivariate signal of each individual kernel was then recorded asin Example 1. No pre-treatment of the first multivariate signals wasapplied and the spectra of each individual kernel were recorded.

[0072] Prior to sorting, a calibration model was established by usingthe unsupervised multivariate method Principal Component analysis on thefirst multivariate signals from the individual kernels in thesub-sample. The first two unsupervised quality variables were thencombined to a second univariate signal, which was used for sorting.

[0073] An example of classifying the kernels into three classesaccording to the second univariate signal is shown in FIG. 4.

[0074] The sorting device was set to separate a withdrawn batch of thenormal baking wheat into three classes (A, B and C as indicated in FIG.4) based on the magnitude of the combined second signal. The classeswere collected in three separate containers.

[0075] Samples from each class were milled and tested for bakingquality. It was found that the flour from class B gave the same bakingresults as the flour from the non-sorted batch. Flour from class C,however, resulted an increase in loaf volume of 30% after baking. Themilling of class A, on the other hand, gave flour with inferior bakingresults in comparison with flour from class B. Furthermore, class A wasfound to be softer and had a lower protein content than the non-sortedbatch. Such a wheat material would be more suitable for biscuitproduction as well as production of müsli-products or feed.

Example 3

[0076] Sorting with Reference to Popping Performance.

[0077] The popping performance of popcorn is correlated to the abilityof each berry to absorb energy, e.g. the microwaves in a microwave oven.The variables behind this performance are so far not fully known.

[0078] A sub-sample of normal popcorn berries was fed to the sortingdevice. The individual berries were distributed into fixed positions asin Example 1 and then exposed to the light from a tungsten lamp. Thelight was chosen in such a way that the transmitted light between 850and 1050 nm could be recorded by means of a diode array. A bandwidth of2 nm was used, the absorbancies were recorded, and for each berry in thedistributor the signal from 100 bands between 850 and 1050 nm was usedas the first multivariate signal. A typical spectrum of a individualberry is shown in FIG. 5.

[0079] The first multivariate signal was pre-treated by using theunsupervised 2. derivative method. Then an unsupervised multivariatecalibration model was established as in Example 2. The firstunsupervised quality variable was used directly as the second univariatesignal for sorting.

[0080] Sorting was performed by using a larger sample of the same batchof popcorn, which was separated into 5 classes (A, B, C, D, and E), eachclass being collected in a container. Samples from classes A, C, and Ewere tested for popping performance and also analyzed as describedabove. A Principal Component Analysis of the spectra in class A, C, andE, respectively, are shown in FIG. 6.

[0081] In a microwave popping test all berries in class C poppedproperly. However, only 1 berry out of 6 from class A and 1 berry out of8 from class E, on average, resulted in a popcorn, i.e. the main part ofthe berries in these classes remained non-popped.

[0082] A class, which is more homogeneous in popping performance, couldthus be obtained by sorting according to the inventive method.

1. A method of sorting objects within a bulk of objects, having aninherent variation, from a heterogeneous population by separating fromsaid bulk at least one class, having less variation than said inherentvariation, which represents a quality of composition with reference toany organic material of said objects, wherein the method comprises thesteps of (a) distributing each of said objects to be separated as aseparate object in a sorting device; (b) exposing said separate objectto energy from at least one energy source; (c) recording from at leastone point of said separate object by means of at least one sensor afirst multivariate signal; (d) predicting or classifying, by means of amultivariate calibration method previously performed on a subset of saidpopulation, between said first multivariate signal and said quality ofcomposition, a second signal expressing the magnitude of at least onequality variable of univariate variation; and (e) separating saidseparate object from said sorting device to said at least one collectedclass in dependence on the magnitude of at least one quality variable ofsaid second signal from said at least one point.
 2. Method as in claim1, wherein before step (d) said first multivariate signal is transformedby means of a supervised or an unsupervised pretreatment.
 3. Method asin claim 1, wherein said quality of composition is an undefined qualityof composition.
 4. Method as in claim 3, wherein said calibration methodis based on an unsupervised multivariate method.
 5. Method as in claim1, wherein said quality of composition is a defined quality ofcomposition.
 6. Method as in claim 5, wherein calibration method isbased on a supervised multivariate method.
 7. Method as in claim 1,wherein said quality of composition is a chemical quality, a structuralquality, a sensoric quality, or a functional quality, alone or incombination.
 8. Method as in claim 1, wherein said emitted energy iselectromagnetic radiation and/or sonic waves.
 9. Method as in claim 8,wherein said electromagnetic radiation and/or sonic waves is ultravioletlight, visual light, near infrared light, infrared light, fluorescentlight, ultrasonic waves, microwaves, or nuclear magnetic resonance,alone or in combination.
 10. Method as in claim 1, wherein said firstmultivariate signal is recorded for a time period of less than 20-30 ms,preferably less than 5 ms, at any point of said separate object. 11.Method as in claim 1, wherein said first multivariate signal is recordedas transmitted, reflected, or emitted energy.